Mortgage Tech: Why Legacy Servicing Systems Can’t Carry Us into the Era of AI 

For more than two decades, mortgage servicers have relied on legacy platforms that were built for a different era—an era defined by static borrower expectations, limited regulatory oversight, and slow technological change. Those days are gone. 

Today, the servicing landscape is more complex and more demanding than at any time in its history. Regulatory scrutiny is intensifying, borrower expectations are rising, digital ecosystems are expanding, and margins are tightening. The systems that once anchored our operations are now the very systems holding us back. 

It should be no surprise, then, that among financial institutions, the legacy modernization market is projected to reach approximately $56.9 billion by 2030 (about a 17.9% CAGR). And the specific market for mortgage servicing platforms (MSPs) was expected to hit $5.5 billion this year, highlighting the increased demand for modern MSPs. The question is no longer if servicers should modernize, but how quickly they can afford to do so.  

The Innovation Gap in Mortgage Servicing 

Legacy servicing systems were never designed for real-time data access, seamless integrations, or automation-driven operations. These systems were many times built as point solutions that no one expected to be around three decades later. Over time, we’ve layered customizations, workarounds, and bolt-on tools on top of them—each one adding complexity, cost, and operational risk. 

Meanwhile, servicing demands have accelerated dramatically: 

  • Regulators expect immediate transparency. Auditability isn’t optional; it’s foundational. And current regulations require real-time or near-real-time access to servicing data.  
  • Borrowers expect digital clarity. Real-time escrow updates, instant communication, and intuitive self-service are baseline expectations.  
  • Data volumes have multiplied. The ability to manage, interpret, and act on that data is now a competitive differentiator. 
  • Workforces require tools that empower. Teams cannot be burdened by manual processes and fractured workflows. 
  • M&A activity is reshaping servicing operations. Consolidation and MSR acquisitions require platforms that can scale quickly, onboard portfolios seamlessly, and integrate disparate systems without prolonged disruption. Legacy platforms were never built for frequent integrations or portfolio expansion at speed. 
  • AI adoption is accelerating operational expectations. Modern AI capabilities—ranging from automated exception resolution and document classification to predictive borrower analytics and conversational servicing—depend on clean data, real-time connectivity, and flexible architecture. Legacy systems limit AI’s potential, forcing servicers to rely on disconnected tools rather than enterprise-wide intelligence. 

The gap between what legacy systems can deliver and what modern servicing requires is widening with every month. Consider recent data from the Software Improvement Group: Approximately 37% of legacy systems earned a “below average architecture rating.”  

Why Modernization Is Now a Strategic Imperative 

Modern servicing platforms do more than replace outdated technology—they reshape the operating model of a servicing organization. 

1. Data Becomes an Asset, Not a Limitation.

Cloud-native, API-driven systems make data accessible, usable, and actionable. Servicers gain real-time insights for risk management, investor reporting, and operational decision-making. 

2. Automation Eliminates the “Exception Culture.” 

Modern architectures support AI-driven exception management, automated QC/QA, and intelligent workflows across escrow, loss mitigation, payment processing, and customer service. 

3. Compliance Moves From Reactive to Embedded.

Instead of scrambling to interpret rule changes or respond to audits, modern systems embed regulatory logic and rule-based workflows directly into the platform. Compliance becomes proactive rather than reactive. 

4. Borrower Experience Transforms Servicer Reputation.

When borrowers can self-serve, understand their escrow, and receive help quickly, customer satisfaction rises—and so does investor confidence. 

5. Scalability Supports Growth and MSR Strategy.

Modern platforms support portfolio expansion, subservicing models, MSR acquisitions, and rapid integration after M&A—without breaking the operational backbone. 

The Real Barrier: Change, Not Technology 

The technology to modernize servicing is ready. The real challenge—and often the real cost—lies in managing the organizational, operational, and cultural shifts required to adopt it. Servicing platforms sit at the center of every function, from payment processing and escrow to customer service, loss mitigation, and investor reporting. Replacing them touches nearly every policy, every team, and every exception path that has evolved over years. 

But modernization doesn’t have to be disruptive. The servicers who manage this transition best do so by treating the migration not as a massive “big bang” project, but as a disciplined transformation program with clear guardrails. Several strategies consistently help control cost, reduce complexity, and protect operational stability: 

1. Rationalize Before You Migrate.

Servicers accumulate a long tail of customizations, shadow systems, and manual scripts—many of them outdated or unnecessary. High-performing organizations simplify before they migrate to avoid dragging technical debt into a modern environment. 

2. Prioritize a “Core First” Approach.

Trying to replicate every legacy workflow inflates timelines. Successful servicers focus first on the core: 

  • Fundamental servicing operations 
  • Investor-critical requirements 
  • Compliance must-haves 

Then they layer enhancements once stability is established. 

3. Build a Dedicated Migration Governance Team. 

Modernization fails when it’s treated solely as an IT project. Cross-functional governance, clear ownership, structured decision-making, and disciplined change control turn a high-risk project into an executable plan. 

4. Use Iterative Data Conversion.

Multiple trial conversions improve data quality and reveal hidden issues early. By go-live, teams have validated outputs repeatedly—turning the final cutover into a predictable exercise rather than a risk event. 

5. Train for Adoption, Not Just Functionality.

The biggest source of disruption is people not feeling ready. Scenario-based training, role-specific workflows, and internal “change champions” dramatically reduce exceptions and lift performance. 

6. Manage Parallel Operations with Precision.

When old and new systems run together, clarity is critical. Strict definitions of ownership, tight reconciliation loops, and temporary automation reduce dual-entry errors and borrower confusion. 

7. Treat Modernization as a Multi-Year Evolution.

Go-live isn’t the finish line—it’s the beginning. Organizations that plan for continuous optimization spread cost over time and steadily increase value through automation, AI integration, and workflow refinement. 

Ultimately, the barrier to modernization isn’t that it’s too big—it’s that too many organizations try to solve everything at once. With the right governance, sequencing, and leadership, modernization becomes not a risk to mitigate, but an opportunity to redefine how servicing operates. 

The Future of Servicing Belongs to the Modernized 

The servicing companies that thrive over the next decade will be those that treat modernization as a business strategy, not an IT initiative. They will be the ones who: 

  • Use data to anticipate borrower needs 
  • Automate to eliminate friction 
  • Build compliance into every workflow 
  • Integrate effortlessly with partners and investors 
  • Adapt quickly to whatever the market demands next 

Mortgage servicing is evolving, and the industry leaders of tomorrow will be defined by their willingness to break from the constraints of yesterday. The organizations that act now will not only improve operational resilience; they will set the standard for the future of our industry. 

Mortgage Tech: 4 Emerging Trends for 2026

The mortgage industry is entering 2026 with more optimism than it has seen in years. After a prolonged period of margin compression, rate volatility, and operational strain, both originators and servicers are preparing for a market that is more stable, more digital, and significantly more consolidation-driven. As a result, mortgage technology strategy—once a back-office concern—has become a front-line priority for executive teams. 

Across the industry, leaders are aligning their 2026 roadmaps around a common theme: technology that drives efficiency, scalability, automation, and resilience. The providers that win will be those that can modernize fast enough to support these demands without disrupting customer experience or compliance. 

Market Trends Driving Technology Adoption 

As lenders and servicers plan for the next cycle, several macro-trends are accelerating investment in digital transformation. Rising operational complexity, evolving borrower expectations, tighter regulatory scrutiny, and the push toward automation are creating a universal need for tech stacks that can do more with less. 

Servicers in particular are moving away from purely reactive technology investments and toward proactive modernization. The goals: reduce manual work, eliminate redundant systems, tighten compliance controls, and ensure technology can scale quickly when volume rises or falls. This also coincides with a shifting sentiment about servicing becoming a strategic enabler vs. a back-office operational burden. These three key market trends are driving technology adoption.  

1. Increased Consolidation Through M&A Activity

This year saw an uptick in general M&A activity; according to Devoe & Company Deal Book, the first half of 2025 surpassed all previous first-half records with a whopping 148 transactions–a 17% jump over the same period last year.

The mortgage industry was no exception. Rather than full-franchise acquisitions, the most visible shift came from MSR activity, where fewer but significantly larger trades drove the bulk of volume. For example, when Lakeview purchased $28.56 billion in MSRs from United Wholesale Mortgage, that single deal represented a 39.6% increase in MSRs transferred that quarter. 

Meanwhile, recent moves by New American Funding and Rithm Capital show a different angle: strategic diversification into insurance and other adjacent financial-services verticals. These deals suggest that mortgage players are seeking new revenue streams, more durable economics, and greater control over the customer lifecycle. 

Experts predict that mortgage M&A activity—primarily consolidation—will remain strong in 2026, driving a sharper need for technology that supports scalability, rapid onboarding of portfolios, and seamless integration of new business units. The push for operational efficiency is prompting servicers to prioritize systems that can normalize data, unify processes, and support large-scale transfers without increasing risk. 

2. More Favorable Market Conditions

According to Fannie Mae, single-family mortgage originations will reach $2.32 trillion in 2026, compared to $1.85 trillion this year. The agency also predicts refinance share will rise from 26% in 2025 to 35% in 2026. While some forecasts, including the MBA’s, are slightly more modest, analysts generally agree: originations should improve in the coming year. 

A few key factors are driving these predictions: 

  • Lower interest rates: Most experts anticipate rate relief in 2026, unlocking pent-up refinance demand. 
  • Increased housing supply: As builders deliver more inventory, home prices are expected to stabilize, improving affordability. 
  • Borrower sentiment: Consumers are increasingly accepting 6% rates as the “new normal,” reducing psychological barriers to transacting. 

As the market rebounds, mortgage servicers will be looking for technology that helps control origination-adjacent costs—particularly systems that streamline borrower onboarding, reduce manual verification work, and improve interactions across the servicing lifecycle. 

3. Greater Confidence in Artificial Intelligence (AI) Capabilities

From our perspective, one of the most interesting trends is the growing comfort with AI-driven servicing technologies. While AI is not new to the mortgage ecosystem, servicers have historically approached it cautiously due to strict regulatory oversight, data-quality challenges, and the high stakes involved in borrower communications. 

That hesitation is fading. After years of proven success in origination, customer service, and document automation, servicers increasingly recognize that AI offers measurable value: faster workflows, fewer errors, lower costs, and reduced pressure on overburdened teams. 

According to Cognizant’s recent survey of non-bank servicers, 74% say they’re investing in innovation to drive differentiation. When survey participants ranked their top three priorities, automation and AI ranked second (32%), behind only platform modernization (37%).  

Where Mortgage Tech Is Headed in 2026 

Given these market conditions, what’s next for mortgage technology in 2026 and beyond?  These four trends will certainly transform the industry.

1. Replacing Rigid Legacy Systems with Flexible, Cloud-Based Software 

The first commercial mainframe systems entered the market in the 1950s—and in the mortgage industry, they never left. While these systems remain operationally stable, they’re deeply inflexible, expensive to maintain, and difficult to integrate with modern technologies. In an AI-first ecosystem, their limitations are increasingly untenable. 

2026 will bring heightened pressure to migrate off these legacy platforms due to rising maintenance costs, increasingly complex compliance requirements, changing borrower expectations, M&A integration demands, and the need to support emerging technologies. 

The next generation of servicing systems will be cloud-native, API-first, modular, and designed for real-time data access—serving as the operational backbone for more intelligent, automated servicing operations. 

2. A Push Toward End-to-End Digitization 

While pockets of digitization exist throughout the mortgage lifecycle, true end-to-end digital workflows have historically been difficult to achieve. Manual workarounds, paper-heavy processes, and fragmented systems often create operational bottlenecks. 

In 2026, servicers will finally close these gaps thanks to: 

  • Improved borrower-facing digital experiences 
  • Operational pressure to reduce cost-to-serve 
  • widespread adoption of API-first systems 
  • The increased frequency of MSR onboarding events 

The result is a shift toward fully digitized processes spanning document intake, payment processing, loss mitigation, escrow management, customer support, and investor reporting

3. Realtime Insights Across the Servicing Lifecycle 

Servicing has always been data-rich, but historically, much of that data has been difficult to access or use. In 2026, we’ll see accelerated investment in data centralization, normalization, and real-time availability. 

Key drivers include: 

  • Demand for real-time insights 
  • Evolving regulatory expectations for auditability and data lineage 
  • The complexity of MSR transfers 
  • The need for high-quality data inputs for AI 

Servicers are increasingly adopting data lakes, event-driven architectures, and advanced governance frameworks that allow them to move from reactive firefighting to proactive risk management and predictive analytics. 

4. More Widespread Implementation of AI Across Operations 

If the past few years were defined by AI experimentation, 2026 will be defined by AI operationalization. Servicers are deploying AI at scale across document processing, workflow orchestration, customer communication, predictive analytics, and compliance automation. 

AI is rapidly becoming the foundational layer that powers modern servicing—reducing manual workload, improving decision accuracy, and creating more resilient operations. Organizations that deploy AI strategically will be better positioned to handle volume variability, regulatory pressure, and rising borrower expectations. 

We’ve already also seen agentic AI quietly coming onto the scene. This technology holds promise for multiple aspects of mortgage servicing operations: 

  • Loss mitigation 
  • Escrow analysis 
  • Intelligent borrower communication and case handling 
  • Compliance monitoring and audit readiness 

The Accelerating Shift Toward a Modern, Resilient Servicing Ecosystem 

In 2026, the organizations that lead the industry will be those that: 

  • Embrace data as a strategic asset 
  • Eliminate manual bottlenecks through end-to-end digitization 
  • Deploy AI thoughtfully and safely across operations 
  • Adopt flexible, cloud-native systems designed for scale and compliance 

The mortgage servicing ecosystem is becoming more dynamic, more automated, and more borrower-centric. For servicers willing to modernize, the coming year represents not just a recovery—but an opportunity to build stronger, more efficient, and more future-ready operations. 

Winning the Race to Go-Live: 4 Tips to Prepare You for Success

We’ve all heard (and possibly experienced) the tropes about failed tech projects. The main culprits are often easy to identify but tough to avoid.  

In a recent BCG survey, business leaders identified three primary reasons for delays and failure:  

  • Lack of clarity or alignment on business outcomes 
  • Lack of realistic timelines 
  • Lack of resources fully dedicated to the program 

With the right approach, your implementation team can avoid these pitfalls. Check out these four winning strategies to kick off on the right foot–and win the race to successful implementation.

#1. Rethink the target process.

The biggest mistakes come from the wrong mindset. If your team is simply looking to do the same thing but faster, it may result in short-term success but also a missed opportunity in the long term. This is the time to transform the business process to capitalize on new capabilities. 

Woman in flying machine with wings and hot air balloon

Take our friend in the image above, from 1870. The author conceived of a flying machine, using birds as the functional model. This invention doesn’t improve flight. Instead, it’s a good example of the same-thing-but-faster mindset, limited by what was available back then.   

Below are some tips to rethink the target process in the context of achieving true transformation:  

  • Map the current process as it really is. This might seem like a tedious step, but it often illuminates redundancies and workarounds that can be eliminated in the new process. Listen to the people who complete the relevant business processes each day–executive sponsors’ process maps usually document how a process “should” go, but don’t include all the workarounds, dead ends, or shortcuts in the real-life business process.  Try to uncover these early! 
  • Align the “to-be” process with desired results. We often find that clients are initially rigid in their concept of what they need, and that they may not be open to another solution—even a ready-made one. Instead of focusing on what you think the new process should look like, focus on the results you’re looking for (e.g., greater efficiency, better visibility, or better accuracy). Your technology partner can then help you map a “to-be” process that fulfills those goals.  This all starts with having a clear picture of the outcome. 
  • Prioritize customizations and enhancements. No technology solution will 100% fit your organization’s unique business process right out of the box. But every new option you request doesn’t need to be available from Day 1. Decide which new features are truly required to complete your business process, and which are “nice to have” enhancements. Your technology partner can create a roadmap that includes all these changes, so you have an approximate timeline for deployment.  It is best to discuss these early to avoid any retrofitting in the future. 

#2. Wrangle your data.

Cowboy on a horse chasing an ostrich

We’ll stick with the 1870’s and consider this cowboy wrangling an ostrich. This isn’t so different from mortgage servicers trying to get a firm grasp on their data!

Every mortgage servicing operation is awash with data: from MSP reports, bank statements, and wire reports, to Snowflake, BDE, and even spreadsheets, the list of potential data sources can seem endless. It’s no wonder, then, that data—or really, lack thereof—often presents a considerable roadblock to successful implementation.  

  • Understand what data you need—and where it all comes from. During one implementation, we discovered that several important data points weren’t included in the data warehouse as expected, so we had to supplement with raw MSP reports. Audit your data to ensure that it all “comes from” the expected source; that it’s complete; and that it’s available when you need it (e.g., daily or monthly).  Getting the full picture early saves time. 
  • Consider data requirements for downstream processes. For example, we often see that Investor Reporting handles all the wires—and then Investor Accounting needs the related loan-level information. Understanding these requirements can help ensure that you don’t “break” any of your data flows during implementation. It can also help you identify opportunities to improve data flow.  For instance, the ability to produce accounting entries as part of our subservicing billing solution came from a request in mortgage accounting, a separate group from our immediate project partners in the operation.  
  • Identify all data “owners.” You’ll need a full picture of your organization’s data ecosystem. Usually, different individuals or teams oversee data security and/or transmission, while others understand the operational context of the servicing data you’re using. You’ll need expertise and input from all of them, even if they won’t be intimately involved in every step of the implementation.  This information is critical for assigning notifications and other workflows to fix any data discrepancies as these are discovered during automation. 
  • Consider any “to be” changes. Will you be using the same data sources for the long term? If there’s a change coming soon (such as changing servicing systems), it may be better to navigate that transition before implementing a new, dependent software system. The answer may not be very clear at this stage.  However, it is important to envision a process to manage any changes to data sources along with a governance plan.  

#3. Dedicate the right resources.

Every member has their own super powers (and version of kryptonite!) Ultimately your people will make or break your implementation efforts. And it’s not just about getting the “right” people, it’s also about ensuring these people have the capacity—and mindset—for the implementation.  

  • Keep your team tight! A team with too few people won’t have adequate time or organizational reach. An overly inclusive team often gets bogged down in minutia. The most effective teams include only engaged, active decision makers and direct stakeholders who understand the strategic and tactical implications of the relevant business processes (and, of course, a stellar project manager!).  The greater team will remain informed, but the core team should remain small and nimble. 
  • Consider the team’s workload. An implementation most acutely affects your super users, who will often be responsible for running parallel processes—they’ll essentially be doing everything twice, for a while. Ensure that these resources aren’t also assigned to other projects or initiatives. In our experience, the most successful (and fastest) implementations are those where at least a few super users are solely dedicated to the implementation and the “to-be” business process.  Fractional schedules where executives dedicate certain hours each month work well.  However, not making effective use of this time (or overextending the commitment) will ultimately lead to lost productivity.   
  • Identify your early adopters. Let’s be real: some employees are going to more resistant to change than others. Avoid putting them on your implementation team and bring them back when the process is more settled. Instead, look for people who have demonstrated fluency in your existing technology, along with eagerness to learn new things and expand their skill set. Ideally, this person will also be a trusted leader (even informally) who can nudge others toward adoption. The excitement will quickly flourish from within. 

#4. Focus on quick wins.

As you plan your implementation, look for opportunities for quick wins. These help bolster your implementation team’s confidence and provide immediate proof of concept to end users. It also gets people into a rhythm of using a new system or running a new process.  This will make introducing bigger changes related to the implementation much easier. 

  • Use the good old Pareto principle. The 80/20 rule consistently plays a starring role in our successful billing implementations. Clients often find, for example, that the majority of their bills fit into the same format (e.g., similar billing lines or remittance packages), so we tackle those first.  These details are typically discussed during Discovery or before. 
  • Start with the easy stuff first. When it comes to implementing the recon system, we almost always start with T&I cashbook and custodial reconciliations. Starting with the least complex elements will give your team the experience and confidence to take on the more complicated scenarios associated with P&I A/A. P&I S/S are usually last since they’re the most complicated and generally comprise the smallest number of recons.  Again, circumstances and priorities will always play a role in how best to plan the implementation. 
  • Go for the biggest time savers. We often find that our clients spend hundreds of hours each month on a single manual process that we can easily automate. In this case, we often tackle that process first, to demonstrate meaningful time savings right away—especially important when your implementation team is still running parallel processes.  

Bonus: Remember, you’re racing toward adoption.

Your work doesn’t end when your new software is up and running. Remember that an implementation is ultimately about adoption—people using that software. The most successful implementations are those where the team focuses on adoption from the very beginning.  

  • Plan engagement with your broader team. Decide when you’ll start transitioning the entire team to using the new software. The implementation need not be complete for this to happen. For example, once we’ve set up the T&I cashbook, end users can get comfortable working those reconciliations in the system while the implementation team moves on to more complicated scenarios.   
  • Communicate early and often. Suprises are great for parties. Not so much at work. The more often you communicate with end users and other stakeholders, the more likely you are to get their cooperation. From the very beginning, explain the relevance of the new technology and how it will improve operations or make people’s jobs easier. It can also be useful to show proof of concept, such as demonstrating a simple reconciliation in the new system.  
  • Create training materials and SOPs as you go. We often find that Super Users will use our standard training materials as a starting point, then tailor them for their organization’s idiosyncrasies. These will give you a jumpstart later, when it’s time to create more formal training materials.  

What are some implementation challenges you faced recently in your organization?  What would you say was the biggest contributor in getting it back on track (or changing directions)? 

Maximizing Software ROI: Top 3 Strategies for Successful User Adoption

Consider this scenario: Your organization just invested thousands of dollars—and hours—on a major software implementation. But you’re still not seeing the efficiency and accuracy improvements you were expecting.  

As you explore the root of the issue, you discover that your team is using the software  to manage recon outages, but they’re still tracking outage owners and expected clear dates in a separate, off-system spreadsheet—a much more tedious process than using the new software for the same purpose.   

You dig deeper and learn that their training didn’t cover that functionality. The team simply kept their same old procedure, unaware that they could automate these components of the process.  

Unfortunately this isn’t an uncommon problem with implementations. A truly successful implementation doesn’t end when the software is up and running; it ends when the end users have competently, confidently adopted the software.  

Failure to Adopt Has Strategic Consequences 

It’s easy to ignore adoption if it’s not integrated in the implementation process. But failing to ensure adoption has short- and long-term consequences for your organization:  

1. Unrealized ROI: This is perhaps the most obvious and immediate impact of a failed adoption. Your organization has invested money, time, and resources in new technology, and now you’re paying for something that no one is using. Furthermore, you’re not gaining the efficiency and savings that should have come with that investment.   

2. Hidden training and support costs: If adoption isn’t baked into implementation, the burden of training and support often fall to the wrong people—namely managers, team leaders, and the IT department. They can quickly get bogged down with redundant, avoidable questions and issues, all of which distract from their primary responsibilities.  

3. Bloated processes: New software should streamline processes. But if your team doesn’t fully adopt that software, they’ll develop workarounds and processing exceptions that bloat SOPs—further complicating training.  

4. Drop in employee morale and attitude: As employees feel defeated or overwhelmed by new software and processes, their morale suffers. This directly correlates with decreases in productivity.  

    5. Elevated risk for compliance violations: When users skip steps or work outside the system of record, your organization can be at risk for expensive compliance violations or audit findings.  

      6. Impaired decision-making capabilities: Most technology adoptions come with the promise of improved insights and data analytics. Without this information, your organization’s leaders lack the comprehensive information they need to make strategic decisions.  

      7. Detours on your organizational roadmap: Lags in adoption not only cost money, but they also ultimately hinder your organization’s digital transformation. Each time an initiative fails to launch, teams lose confidence, roadmaps slip, and eventually your entire strategic vision gets derailed.  

      Although there’s no foolproof method for ensuring a successful adoption, there are several principles that will increase your chances for success.  

      #1. Address process governance, not just process management.  

      In our experience, the most successful adoptions are those where the organization embraces not just process management, but true process governance. What’s the difference?  

      Process management is essentially tactical, ensuring that individuals follow the correct process. Generally supervisors or team leads handle process management, and they may not have complete insight into how their updated business processes impact other upstream or downstream business processes.  

      Process governance, on the other hand, is more strategic, with multiple goals: 

      • Ensuring that new processes align with overall business goals 
      • Fulfilling all relevant regulatory or operational requirements (especially important in mortgage servicing) 
      • Implementing the new process correctly and consistently across all impacted business teams 

      The ideal process governance structure is usually federated: individual teams have autonomy over process management and provide input, but a single team or leader is responsible for overall governance structure.  

      While it may require more time during the initial phases of implementation, establishing a robust process governance framework better ensures proper adoption. Here’s a (non-exhaustive) list of some key elements:  

      • Clearly defined roles and responsibilities: The most effective implementation teams fully understand what they “own” in the process. Your process governance plan should clearly outline who’s involved when, and what their responsibilities are. 
      • Detailed SOPs for the current and “to-be” processes: We frequently find that employees have developed their own—often undocumented–workarounds or shortcuts for current processes. It’s critical to understand these, and to document what the new process should look like.  
      • Thoughtful communication strategy: The best communication plans use the mantra of “Early and often.” Start before the implementation is too far along, and consistently communicate the business objectives of the new process. Tailor the messages for different stakeholders; end users will have different concerns and goals than senior leadership, for example.  
      • Relevant KPIs and metric tracking: It’s critical to define the success of any project, and a technology adoption is certainly no exception. Frequent benchmarking allows you to identify pitfalls or challenges as early as possible. More about setting KPIs below!  

      #2. Track the right KPIs and metrics.  

      As a business leader, you’re probably laser focused on the ROI of any technology adoption. And you should be! Often we see business leaders who are hyper-focused on the numbers that come at the end of a successful adoption (such as reduced headcount or decreased costs). While those numbers are certainly important, they don’t provide the insights you need in the interim.  

      Since we’re talking numbers, here are a few eye-opening stats: According to the Technology Service Industry Association (TSIA), 70% of software features don’t get used by customers. And the Whatfix 2024 Digital Adoption Trends Report concluded that 78% of employees lack expertise and knowledge of the software they use daily, and could use more training.  

      So when it comes to adoption, which numbers matter? Focus on the KPIs and metrics that demonstrate true user value:  

      • Workflow completion rates: This is usually defined as the percentage of employees who are using the new technology to complete the relevant work, rather than the old process.  
      • Time-to-task accuracy: How long does it take employees to do their tasks accurately and error-free using the new technology? This figure should decrease as employees gain more fluency with the new process.  
      • Support ticket volume: It’s natural for users to submit a higher volume of support tickets for a new technology. But if support ticket volume remains static over time, it usually indicates that users need additional training or other support.  
      • Use of workarounds and shadow tools: This can be more difficult to measure, but it’s important to work with your team leaders to track this behavior. 
      • Time to proficiency: How long does it take a new user or employee to gain fluency with the new process, as indicated by independent completion of the business process?  
      • Process cycle time: How long does it take to complete the end-to-end business process? Shorter cycles indicate improved efficiency. 
      • Employee productivity rate: How productive is each individual employee, as measured by their tasks completed each day (e.g., number of reconciliations submitted per day)?  
      • Organizational productivity rate: How productive is each team, as measured by tasks performed each day? These gains indicate improved organizational efficiency. 
      • Error rate: How often do employees make errors or require assistance to complete their work? High error/intervention rate often means more training is necessary.   

      Skip the vanity metrics here! The number of active users or total clicks is hardly indicative of how users are really using the new technology. For instance, high total clicks could actually demonstrate that users don’t know how to use the new system and spend lots of time clicking around, looking for the right things.  

      #3. Plan for adoption after the implementation ends.  

      So the implementation is over. That means it’s time to move on to the next project, right? Not so fast! While the “main” adoption might be over, adoption efforts must continue.  Consider these three common scenarios:  

      • New employees join the team: Who is responsible for training new employees to use this technology? What does that training look like? Which KPIs will be used to determine when a new employee has achieved proficiency? And who is responsible for updating training materials as processes change or new features are introduced? All of these answers should be documented before you close out the implementation.  
      • Business processes evolve: Perhaps your organization acquires a new set of loans, and now you must meet a different set of agency requirements. Your vendor can add the necessary configuration, but your end users will still need to understand and use the new functionality. Or you identify another downstream business process that could be automated using this same technology. A new team will need to start from scratch to learn a new process. In both these situations, existing training materials will be a starting point, but they’ll need to be supplemented using a new adoption plan.  
      • The vendor releases new features: As technology partners, we work closely with our clients to determine which new features will add real value. We also provide how-to guides and walkthroughs to introduce new functionality to superusers and business team leaders. Our efforts can only go so far, and we need our clients to champion the adoption of new product features so they can achieve the desired value and results.  

      Address these scenarios during your implementation, ideally as part of your overall process governance. This will ensure smoother transitions for new employees, greater operational efficiency when processes change, and increased adoption of new features that can improve value.  

      Reinventing Investor Reporting: How Automation Solves Servicer Headaches

      Investor reporting has always been one of the most complex and high-stakes tasks for mortgage servicers. Between multiple investor formats, strict timelines, and ever-evolving compliance rules, the pressure to “get it right” has never been higher. 
       
      But here’s the truth: manual reporting processes just can’t keep up anymore. 

      The Legacy Pain Points 

      • Manual data aggregation from servicing platforms 
      • Excel-based validations and reconciliations 
      • High error rates and rework cycles 
      • Stressful compliance audits with limited transparency 
      • Inability to scale as portfolios grow 

      Enter Automation 

      Investor reporting is being redefined by automation—and it’s a game changer. 

      • System Integration: APIs and ETL tools consolidate data across servicing systems, LOS platforms, and investor portals. 
      • Real-Time Validations: Custom rule engines flag issues before submissions, reducing investor kickbacks. 
      • Dynamic Templates: Investor-specific formats are pre-built and updated automatically to meet GSE and private investor standards. 
      • Audit Trails Built-In: Every action is logged, tracked, and easily auditable—hello, stress-free compliance. 
      • Scalability: Grow your portfolio without growing your team. Automation adapts with you. 

      Automation in Action: Two Real-World Examples 

      Example 1: Changing the Delinquency calculation formula 

      Suppose there’s a formula to calculate the delinquency category for investors. If you decide to tweak this formula you’d traditionally have to: 

      • Open each investor template manually 
      • Update the formula one by one 
      • Validate the logic separately for each investor 

      This process could take hours—or even days—depending on the number of investors. 

      With automation? 

      Make the change once. 
      Click
      And the update applies instantly across all relevant investor templates, and you can produce as number of bills this change affects. Simple, fast, and consistent. 

      Example 2: Trial Balance Validation 

      Suppose you need to validate trial balances across multiple investor reports. Traditionally, you would have to: 

      • Open each report individually 
      • Perform a manual calculation to verify difference between balances and collection are correct 
      • Flag and review any mismatches one by one 

      This process is tedious and time-consuming—especially across large portfolios. 

      With automation? 

      • Run the calculation in bulk 
      • If the result is 0, the trial balance passes 
      • If not, the system flags the error instantly 

      No more manual digging. Automation identifies discrepancies across all investors in minutes, saving hours of work and reducing the risk of oversight. 

      Why It Matters

      Mortgage servicers who embrace automation are not just gaining speed; they’re building resilience in their operations. 

      Key Benefits:

      • Faster close cycles – Close deals quicker without the bottleneck of manual data entry. 
      • Improved accuracy – Minimize errors that arise from manual processing. 
      • Happier investors – Increase investor satisfaction by submitting accurate reports on time. 
      • Lower operating costs – Reduce costs related to manual work and overhead. 
      • More time for strategic work – Free up resources for higher-value activities instead of repetitive tasks. 

      Final Thought 

      If your investor reporting still runs on spreadsheets, now’s the time to rethink. 
      Automation isn’t the future—it’s already here. 

      Sources & References 
      • Mortgage Bankers Association (MBA): www.mba.org 
      • Fannie Mae Servicing Guide: https://servicing-guide.fanniemae.com 
      • Freddie Mac Investor Reporting: https://guide.freddiemac.com/app/guide/section/8102 
      • CoreLogic: www.corelogic.com 

      Servicer-to-Servicer Loan Transfers: A Data Story

      A Servicer’s View Into the Investor Reporting Impact 

      If you’re part of a servicing team or work with investor reporting, you’ve probably felt the ripple effects of a loan transfer. Maybe it started with a spike in data checks, a rushed cutoff reconciliation, or a record update that spiralled into a week-long scramble. 

      But what really happens when a loan gets transferred? And how does it affect investor reporting behind the scenes? 

      On the surface, a loan transfer may look like a simple system update. In reality, it’s a highly coordinated handoff involving data accuracy, compliance, and investor assurance. For teams across loan boarding, servicing data, escrow administration, and cash management — as well as those handling investor reporting, accounting, borrower communication, and legal compliance — this moment demands laser-sharp precision, timing, and cross-functional coordination. 

      Let’s unpack it from the servicer’s side of the fence— and explore how to manage these transitions like a pro. 

      What Is a Loan Transfer, Really? 

      A loan transfer occurs when the servicing rights for a mortgage loan move from one servicer to another. This can happen for several reasons—portfolio realignments, mergers, investor mandates, or performance concerns. 

      It might sound like just moving a file from one cabinet to another—but in servicing, it’s anything but simple. 

      Behind the scenes, multiple teams get involved: 

      • Data integrity teams ensure records are clean and complete. 
      • Investor accounting teams reconcile balances. 
      • Investor reporting wraps up financials and finalizes the story of the loan. 

      This is more than just an operational shift—it’s a high-stakes moment with long-term implications for borrowers and investors alike. 

      Why Should You Care as a Servicer? 

      A loan transfer is your final chance to ensure everything is in order before handing it off. This is a moment of accountability, and any errors can ripple into downstream issues for the new servicer, the borrower, and the investor. 

      Here’s what you (as the outgoing servicer) must deliver: 

      • Accurate and Complete Loan Data 
      • Full Transaction History – Payments, adjustments, servicing activities 
      • Reconciled Principal, Interest, and Escrow Balances 
      • A Final Investor Report showing the loan’s ending position 
         

      this handoff builds investor confidence and reinforces your reputation for accuracy. Done poorly? It can lead to audit flags, operational delays, and unhappy stakeholders. 

      Why It Matters to the New Servicer

      For the incoming servicer, a loan transfer represents more than just acquiring additional accounts—it’s a critical opportunity to demonstrate operational strength, borrower care, and investor trust from day one.  

      When a loan is newly boarded, the clock starts ticking immediately. Payments may be due, escrow disbursements may be pending, and borrowers may already have questions. The new servicer must be ready to step in seamlessly—because any delay or confusion at this stage can erode trust quickly. 

      Key Priorities for the New Servicer: 

      • Accurate Data Onboarding: Ensuring clean and complete loan data prevents system errors and borrower confusion. 
      • Prompt Borrower Communication: Clear, early messaging builds trust and avoids missed payments. 
      • Operational Readiness: Internal teams must be aligned to handle escrow, payments, and reporting without delay. 

      Common Challenges for the New Servicer—and How to Address Them 

      Challenge How to Address It 
      Incomplete or inaccurate data from prior servicer Perform rigorous pre-boarding validations and clarify gaps with the outgoing servicer early. 
      Borrower confusion or complaints Send timely, personalized welcome communications, including transfer confirmation, new payment address, and instructions. 
      Escrow or P&I reconciliation gaps Align on reconciliation protocols and validate all balances before the first disbursement. 
      Investor reporting inconsistencies Coordinate with internal investor reporting teams on boarding schedules, cutoff dates, and transfer tagging to ensure continuity. 

      What Does Investor Reporting Do During a Transfer? 

      Investor reporting acts as the bridge between daily servicing and investor confidence. Here’s what you need to focus on: 

      Action What You Should Do 
      1. Cut Off Cleanly Prepare the final reporting cycle up to the transfer cutoff date. Avoid overlap or duplicate data. 
      2. Reconcile Everything Confirm all payments and balances such as principal & interest payments, escrow payments, escrow balance, escrow advance balance, recoverable corporate advance balance, non- recoverable corporate advance balance, suspense balance, restricted escrow balance, replacement reserve balance – are accurate and final. 
      3. Mark the Transfer Update the system: transfer date, new servicer, investor info, and “Transferred Out” status. 
      4. Communicate Clearly Notify all key parties: investors, custodians, and internal teams. Alignment is key. 

      What Could Go Wrong—And How to Prevent It 

      Loan transfers have many moving parts. Here are the top pitfalls and how to avoid them: 

      • Missing Cutoff Dates 
        → Align early with the incoming servicer on the exact transfer date. 
      • Unreconciled Advances or Escrows 
        → Verify and document every balance before cutoff. 
      • Data Mismatches 
        → Cross-check loan system data with final investor-facing reports such as Investor Reporting Package, Investor Remittance Report, Loan Trial Balance Report, Transaction History Report, Data Tape Report, Mod Tape Report & Loan Status Report. 

      Best Practices for a Smooth Transition 

      Here’s how to make your next loan transfer seamless: 

      • Start Early: Begin planning 30–60 days in advance. 
      • Coordinate Across Teams: Sync with internal ops, custodians, and the incoming servicer. 
      • Validate Everything: Double-check loan balances, payment history, and transfer flags. 
      • Archive Confidently: Store all final investor reports securely for future audits. 

      Final Takeaway 

      • Loan transfers can feel hectic, but they don’t have to be messy.  
      • For servicers, it’s about delivering a clean, reconciled loan file— like a well-packed suitcase, organized and ready for the next stage.  
      • For the investor reporting team, it’s all about transparency, accuracy, and delivering a final snapshot that instils confidence and build trust through clear, compliant reporting.  

      When done right, it’s a smooth baton pass—not a stumble. 

      4 Ways that AI Drives Opportunity for Mortgage Servicers

      Artificial intelligence (AI) is reshaping the mortgage servicing industry, offering innovative solutions to long-standing challenges. AI is poised to have a transformational impact on automating tasks, enhancing decision-making, and improving the borrower experience.

      Four Key Areas for AI in Mortgage Servicing

      While AI has myriad applications across the mortgage servicing lifecycle, these four domains present rich opportunities for process transformation:

      1. Customer support
      2. Investor Reporting
      3. Risk management
      4. Portfolio optimization

      1. Customer Support

      Customer support is perhaps the most common application of AI in the mortgage industry and will certainly get more robust as the technology matures.

      • Chatbots and virtual assistants handle common borrower inquiries 24/7, reducing wait times.
      • Sentiment analysis monitors borrower interactions to identify satisfaction levels and address concerns proactively.
      • Personalized assistance builds on chatbot technology, recommending individually tailored solutions such as repayment plans or refinancing options.

      Use case: AI-driven chatbots can answer frequently asked questions, freeing up human agents to focus on more complex borrower needs.

      2. Investor Reporting

      AI simplifies the complex requirements of investor reporting, ensuring accuracy and compliance.

      • Data aggregation automatically collects and consolidates data from various sources, such as the servicing system or bank statements.
      • Error checking identifies discrepancies in data before submission.
      • Automated report generation ensures that reports are delivered in accordance with investor timelines and regulatory guidelines.

      Use case: AI-driven platforms can prepare accurate, comprehensive investor reports in a fraction of the time it takes manually, reducing operational bottlenecks.

      3. Risk Management

      Machine learning and generative AI effectively power diverse risk management efforts.

      • Automated data analysis identifies potentially fraudulent anomalies in borrower behavior or payment patterns.
      • Compliance monitoring ensuring adherence to regulatory requirements through automated audits.
      • Default prediction uses machine learning models to identify borrowers at risk of default.

      Use case: Predictive analytics can flag loans with a high likelihood of delinquency, allowing servicers to take preventive measures.

      4. Portfolio Optimization

      AI-powered predictive analytics allow for proactive decision-making, more effective portfolio rebalancing, and strategic pricing, along with identifying relevant trends in real time.

      • Predictive insights forecast market trends to inform portfolio strategies.
      • Dynamic adjustments rebalance portfolios based on changing borrower behaviors or economic conditions.
      • Performance tracking monitors KPIs to ensure optimal portfolio health.

      Use case: Machine learning models can recommend adjustments to servicing strategies based on real-time portfolio performance data.

      Industry Leaders Weigh in on AI in Mortgage Servicing

      We recently asked industry leaders to share their predictions about the future of AI in mortgage servicing. At the 2025 MBA Servicing Conference in Dallas, our booth visitors could cast their votes for which domain would see the most impact from AI.

      The overwhelming winner? Customer support. This makes perfect sense, given that AI has already been widely adopted for this function. For example, Bank of America’s chatbot Erica has reportedly had more than 1.5 billion interactions since its launch in 2018.

      Meanwhile, several visitors were skeptical about AI for investor reporting. They cited the complications and intricacies of this business operation. But in our view, AI holds untapped opportunity for investor reporting. Similarly, AI will also enable more robust portfolio optimization as the technology matures.

      How Is Integra Contributing?

      Integra is committed to adopting AI technologies responsibly and ethically. In the coming year, we’ll be launching multiple AI initiatives across our software suite.

      • AI-guided configuration and processing: In the Integra INVESTOR application, a trained AI assistant will guide users through configuring the system and running business processes. The next phase will incorporate department procedures to ensure standardization across analysts.
      • Recon assistant: The system will also suggest possible reconciliation matches and custodial breaks for the analyst to consider. The model will also check the data inputs to assist Super Users handle processing exceptions.

      Auto-configuration based on contract: In the Integra BILLING application, subservicing contracts will drive the configuration requirements via automation to produce the monthly invoice and remittance package.  Analysts will verify the calculations but no longer will need to know all the mechanics of the application. 

      [List] 5 more best practices for custodial reconciliation

      Thanks to all the response I got from my previous article, Best Practice: 5 Considerations for Custodial Reconciliation, I was inspired to share 5 MORE Considerations for a sound Custodial Reconciliation process.  Please share your thoughts and let me know what you think of this list.

      6. Clearly classify current reconciling items into research buckets.    

      Aside the standard TOEC formulas, an additional set of calculation that may be included within the Bank Reconciliation process is an automatic classification of reconciling items by research bucket. This classification applies to current outages and is intended to help analysts in their research activities when identifying reconciling items. The following list represents a highly generalized classification of reconciling items classified by research category using data that should already be present to fuel the process. Far more refined research categories (and perhaps even automatic reconciling item identification!) may be accomplished depending on the overall quality, consistency and detail available within the source data inputs. The research categories provided below also represent a hierarchy meaning that items are classified as they meet the criteria of each bucket per the order below:

      • Paid in Full – Check for ending actual remittance balance to be zero along with a payoff date provided in the source data.
      • Liquidation – Applies to S/S remittance deals only. This classification is given when the actual remittance balance is zero and both the beginning and ending scheduled remittance value is not zero.
      • Reinstatement – Check for the beginning scheduled remittance balance to be zero and the ending scheduled remittance balance to not be zero. Also check for the beginning actual balance to be zero and the ending actual balance to not be zero as either of these conditions can be true for a reinstatement.
      • Modification – Simple; check if there is a modification date provided in the data.
      • Stop Advance – Similar to Modification; check for a stop advance date provided in the data.
      • Miscellaneous – This category catches any outages not linked to a research category above.

      7. Apply a standard description to reconciling items.    

      This one is key. Any outages or reconciling items resulting from the Bank Reconciliation process should be identified and categorized using a standard description that is meaningful to the business (i.e. reason codes). We suggest defining a comprehensive list of coded values representing all the different types of reconciling items in a typical reconciliation cycle. For example, consider grouping all reconciling items related to liquidations under a standard notation – LIQ1 to represent liquidation net loss, LIQ2 to record a service fee outage, and so forth. Another important detail to add to this master list of standard descriptions is the expected resolution type – in other words, if the item is expected to be resolved via wire, remittance adjustment or perhaps a system-level adjustment as would be the case for non-cash outages. Keeping in discipline with this consideration helps in fulfilling #8 below.  

      8. Meticulously clear ageing reconciling items; start with the oldest first.    

      It is both tempting and (theoretically) time effective to simply bump the list of reconciling items against wires /remittance adjustments by amount and delete these off the spreadsheet as resolved items. Unfortunately, any virtue found in this approach quickly goes away when a discrepancy is identified a couple months later (i.e. clearing a wrong item) and an analyst is tasked with trying to unravel the components in an effort to correct the issue. We recommend implementing a mechanism for tracking the resolution of reconciling items which also ensures that the correct wire /remittance adjustment is paired with the intended outage. Adopting a practice of applying standard descriptions along with an expected resolution type as suggested in #7 addresses the first part of this recommendation. A solution to the second part of the recommendation related to pairing wires /remittance adjustments to outages is offered under #9 below.    

      9. Optimize Wire /Remit Adjustments for future clearing.   

      This suggestion may require some coordination to accomplish and some discipline to maintain, but the added value of this effort will be well worth the work. The simplest and most effective way to properly pair wire /remittance adjustments to corresponding reconciling items is to link these together using a common reference number. Implement this consideration by assigning a unique reference number to outages identified during the current period. If a standard description and corresponding resolution type is assigned to each reconciling item as suggested in #7, a listing of required wires and remittance adjustments should be readily available at the conclusion of each Cutoff. Passing along this unique reference number to the wire /remittance adjustments request as the transaction identifier creates an immediate link between both items that can be leveraged for clearing. The real trick in having this work is convincing the downstream processors (i.e. Investor Reporting and Treasury or team responsible for wires) to include this value as part of their process from request through transaction settlement. As an extra credit bonus, include a unique identifier for these transactions at the account-level as well (i.e. remember, items in TOEC are at loan /pool-level but these settlements typically disburse as a rolled-up transaction by bank account). This additional step will save a lot of time pairing bank statement items to corresponding book wires, thus enabling book-to-bank reconciliation for Cashbook.

      10. Track and measure the process.    

      All the considerations leading up to this one focus on ensuring a sound Bank Reconciliation end result, which is fantastic. However; visibility and metrics gathering over the process as it is happening in real-time distinguishes a proactive team vs. a proactive team. What’s the difference? A reactive team sees smoke and eventually reaches the fire with whatever tools happen to be on-hand to try to extinguish the flames, and a proactive team sees the spark that started the fire – this level of visibility is afforded by adopting well-defined work assignments and developing a dashboard to track the resulting metrics. We recommend doing what most companies already do: create a spreadsheet to assign analyst resources to specific Bank Reconciliation reports, but we push it one step further by suggesting the inclusion of triggers to track the progress within a Cutoff as it is happening. Create a spreadsheet or tool that listens for status changes in Bank Recon reports (i.e. Pending to Approved) as well as a means to collect metrics (i.e. number of reconciling items by ageing or number of items resolved vs. outstanding) in an effort to get a meaningful pulse of the process as a whole. The development of the dashboard is certainly an evolutionary process; the trick is to subscribe to this mentality or management overview philosophy if the terminology is more fitting. Either way, evaluating the health of a process needs to occur as the process is happening and not after the process is completed – test this statement by applying it to a living body. Find creative metrics (and corresponding triggers) to track the process as it is unfolding to prevent a spark from becoming a forest fire.

       What considerations can you share about how you manage your Bank Reconciliation business process?

      [List] 5 best practices in custodial reconciliation

      An accurate and effective Custodial Reconciliation process is the cornerstone of a healthy Investor Accounting function.  Completing a monthly Bank Reconciliation for each Custodial account (i.e. P&I and T&I) accomplishes two important goals: (1) it confirms the Custodial bank account is in balance at the aggregate level; and (2) it ensures the individual loans /pools within the Custodial account are also in balance by performing Test of Expected Cash (TOEC) calculations.  Every company servicing loans in-house adopts some sort of Bank Reconciliation process – after all, it is a compliance requirement under Regulation AB.  Here are 5 considerations for building an optimum Bank Reconciliation business process based on our experiences working with companies like yours:

      1. Clearly define Cutoff start and end dates.    

      I know it sounds intuitive, but we’ve seen this mistake consistently – make sure that there is no overlap between Cutoff start and end dates as you define processing calendars. More importantly, verify that all activity considered by the process is restricted to this range; that is, all wires, remittances, remit adjustments, servicing data and investor reporting inputs must fall within the criteria. Not following this simple guideline will lead to a lot of transactional “noise” and incorrect TOEC calculations.

      2. Roll from a previous period.    

      Again, it may sound intuitive but it is surprising the number of companies we’ve seen that essentially “start anew” with their Bank Reconciliation process. Lesson learned – live with your results (and calculations). The true power of a Bank Reconciliation summary is in rolling it forward; in other words, tie together Beginning Balance from the current period to Ending Balance of the previous period. The value of this practice is accentuated for PLS. For these reconciliation reports, the following balances should roll forward from a previous period: (a) cashbook balance; (b) beginning scheduled balance for the expected remittances; (c) beginning actual balance per the actual UPB; and (d) actual remittance rolled forward from last period’s expected balance.

      3. Ensure the bank account is in balance before digging into loan /pool level balances.       

      Often overlooked and oversimplified, the Cashbook process serves a fundamental purpose in achieving an accurate and effective Bank Reconciliation process. It is important to actively balance the custodial bank account as a precursor to performing reconciliations at the loan /pool level rather than assuming than any discrepancies will simply float to the surface.  

      4. Perform simple data integrity checks.    

      Because we are working with two related but DISTINCT data sets when performing Bank Reconciliations (i.e. bank statement and cashbook vs. servicing and investor reporting inputs), there are some simple data integrity checks that can help verify that source data is complete and accurate before relying on these values for balancing. It is a good idea to perform the following sanity checks: 

      • Test that wire and remit adjustment amounts collected at the loan /pool level roll up to the amount reported at the bank account level. In theory, these values are extracted from the same transaction set, however; invalid translations /mappings between bank accounts and associated loans /pools can lead to different results. The potential for this discrepancy is magnified as the volume of bank accounts and loans /pool increases.    
      • Perform a Custodial Reconciliation Difference calculation to verify that all necessary inputs are collected in the Test of Expected Cash calculation. The sum of (a) P&I advance; (b) remittance adjustments; and (c) current period outages /reconciling items should equal zero, thus certifying that Servicing and Investor Reporting inputs are captured completely and accurately.

      5. Have a clean TOEC formula.    

      A whole new article could be written about best practices regarding TOEC formulas given the intricacies between PLS vs. GSE and considerations within Sch/Sch, Sch/Act and Act/Act remittances. For argument’s sake, let’s assume a consensus that a TOEC formula, at its most high level, should include (a) prepaids;  (b) delinquencies; and (c) remittance information (i.e. scheduled interest and principal, additional principal collections, etc). For extra credit, you could include curtailments and other items but these would point to a specific type of deal. More to the point, a TOEC formula SHOULD NOT include remittance adjustments as part of the calculation.  Including items such as HAMP incentives, claims and refunds, interest adjustments and other similar items unnecessarily abstracts the meaning of the calculated figure and misrepresents any true discrepancy in cash. In addition, including any of these items in the calculation prevents a clean roll-forward (see #2 above) particularly when one of these “expected” items does not materialize for whatever reason.  

      The Big Data waste: Missed opportunities in mortgage servicing

      The term “Big Data” has been buzzing about the technology industry for several years now and has crept itself into the vocabulary of business managers and corporate execs to mean the new must-have for the modern enterprise. Even though there may be great promise in the use of Big Data to solve complex business problems, I find that the concept itself is largely misunderstood. Making matters worse, Big Data has also become somewhat synonymous for “Big Company” and “Big Budget” – a luxury reserved for the Fortune 500 with large slush funds to spend on consultants to help figure out whatever alchemy Big Data is intended to accomplish. There certainly is truth to that, but the landscape is rapidly changing. With an increase in accessibility and simplicity of tools designed to wield Big Data into something meaningful, just about any company that produces data can use it to its advantage.

       As a software and service provider in the mortgage industry, I can confidently say that the volumes of data produced by companies in this space are astronomical, to put it mildly. I also dare to say that, for most of these companies, the wealth of information that could be extracted from this data is completely wasted. After speaking to a couple executives and frontline managers about their perceptions on Big Data and its analysis, I see a pattern contributing to the active disinterest toward exploring data sets:

      • General misinformation about Big Data, and all that comes with that, and
      • A lack of imagination about how data can be used to make a direct and meaningful impact on operations.

      Perhaps it is far too ambitious to say that this article will solve both (or either) of these issues. But… maybe a brief introduction to Predictive Analytics and how these can be applied can help prompt a shift in this mentality. It is all about knowing how to ask the right questions.

      What is Big Data Anyway?

      Unceremoniously, Big Data is a large data set. Enormous actually. More specifically, it is a data set that is so large it requires special technologies to house, manage and analyze the information, as conventional tools prove inadequate or impractical. The term, however, has been expanded mostly thanks to marketing efforts of firms offering services in this space to also include the methods and practices available to interpret the data. Each marketing piece and slogan developed around Big Data has contributed to obfuscating its definition while brining some level of very marketable mysticism. Sales pitch aside, Big Data is the collective term for the troves of information produced across an enterprise.

      What can Big Data tell us?

      Well, it really depends who wants to know and for what purpose. That’s the key – identifying a specific purpose. Without clear ideas linked to measurable results, looking into Big Data is like getting a bunch of answers for which there are no questions; i.e. lots of information about nothing we care about very much. It is hard to come up with these questions. It is even harder when we don’t know how best to frame these questions to get the meaningful answers we might be expecting. I’m not a data scientist, and frankly, some of the theories and a lot of the math behind Big Data is beyond my grasp. I think in business processes and software-driven solutions. Learning about Predictive Analytics and its application has completely changed my attitude about Big Data, opening up a new playground of productivity. Here’s a little about how it works and how best to start thinking about applying Predictive Analytics so you can start phrasing your own questions.

      Predictive Analytics = Forecasting the Future

      Forecasting the future is not the same as seeing the future. Predictive analytics uses the sorcery of mathematical modeling and machine learning to predict the outcome of specific scenarios given some data inputs related to the process. The mathematical modeling component helps measure the likelihood of the different scenarios happening given past and fresh data inputs. Machine learning is the really exciting piece; it takes into account historical outcomes to better predict the likelihood of different scenarios, AND even predict new scenarios given patterns and nuances that only machines can identify in the data. So, the wrong way to ask Big Data a question is “Will [this scenario] happen?” – that’s seeing the future. An appropriate question for Big Data would be worded as “How likely is [insert scenario] of happening?” or “How much more likely is [this scenario] to happen instead of [this scenario]?”.

      Opportunities Missed

      For an industry that so heavily relies on data, it is somewhat crazy to me that this same love for data has not extended to using predictive analytics. The possibilities are virtually endless, only limited by the process owners’ imagination. Below are some quick ideas of questions I’d be asking Big Data to help tighten business processes and operations, albeit, from the high level perspective of a solution provider to the industry rather than the pointed precision expected from a business process owner:

       In Originations:

      • How likely is a candidate to close on a loan? How long with the process take? Where in the process chain can we expect hold-ups?
      • What loan products will work best for a particular group of prospective borrowers? How many post-close problems can we expect? What percent will represent buybacks?
      • How will volume be affected given a new incentive or program? Over time, does the metric hold true?

      In Servicing:

      • How many loans are likely to have modifications, delinquencies or become paid in full? What factors are directly contributing to the portfolio performance?
      • What population of loans will have reconciling items? What is the expected source and resolution of these items? How many of these items hit 90 days or go to Reserve?
      • How many errors can we anticipate in a given business process? Where will these errors likely come from? If we implement a change, what might be the effect? Once implemented and over time, will these assumptions hold true?

      These are a handful of questions in just two areas within the vast world of mortgage operations. Now that you have a framework for how to ask questions of Big Data, what would you like to know? How would you manage if you could predict the future? Where would you invest capital? Would you buy a lottery ticket? Wait, Big Data and predictive analytics cannot see the future.

      Showing Cashbook Some Respect

      Often overlooked and mostly oversimplified, the Cashbook process presents an important opportunity for reducing rework and increasing efficiency in Custodial reconciliation. During more than one occasion, I’ve heard people in Investor Accounting call it a mere formality; a means for validating the depository balance. Some have gone as far as not considering Cashbook its own process at all, but simply a data input to the real star of the show: the Test of Expected Cash (TOEC). 

      Their rationale? Any outages in the account would just fall out while calculating the loan-level TOEC, so performing a full Cashbook reconciliation seems somewhat redundant.  I tend to agree, in principle. However, my experience has proven the opposite in certain situations. Any time savings gained in abbreviating the Cashbook process are more than lost when researching certain outages in TOEC.

      At a basic level, the goal of Cashbook is to ensure the Custodial bank account is in balance. At a deeper level, the Cashbook process presents an optimal tool for certifying the bank statement (i.e. via performing a transactional book-to-bank reconciliation). This is good because collections, for example, recorded in the Servicing system would match deposits in the bank statement with any discrepancies falling out as reconciling outages. Yes, TOEC should catch these same discrepancies. 

      How about this scenario: a wire is coded incorrectly and ends settling within the wrong P&I account? The TOEC process should also catch this, but the outage would not be linked to loan-level activity as it is an account-level item. In a sophisticated TOEC process, the outage may be caught early without missing a beat. If the process is not designed to specifically handle these scenarios, things start getting ugly. It may take analysts a lot of extra digging to identify why loan-level activity does not match up with the account balance. 

      Also, consider how this outage would be recorded in TOEC. Is there an appropriate root-cause category code for it? Maybe; probably not. Lastly, consider timing (chronologically, not Reg-AB time). By the time the outage is identified in TOEC, this money may be in the incorrect Custodial account for 30 (maybe even 60) days, idle. Then, depending on the process, it may take another 30 days to initiate the transfer and move the money. Another good example for wasting time in TOEC: researching and correcting a true bank error.

      From my perspective, all this could be avoided with a disciplined and well-structured Cashbook process; a proactive approach to handling account-level items that get resolved before they reach TOEC. It is time to show Cashbook some much well-deserved as past-due respect. In honor of this neglected business process, I am proposing 5 considerations for building a sound practice within your operations:

      1. Clearly Define Start and End-date Parameters   

      Avoid the common mistake of overlapping Cutoff start and end dates by double-checking data filtering parameters. This could get tricky as not all Cashbook reconciliations fall on month-end (think FHLMC) and processing cycles do sometimes become extended to work on a non-business day. In other words, verify that all activity for the bank statement is restricted to this range and that no book transactions enter this process ABOVE the defined range (consideration #3 below will explain why some book transactions from the previous period should be considered in the process). Not following this simple guideline will lead to a lot of transactional “noise” and a disorganized Cashbook reconciliation.    

      2. Roll from a Previous Period

      This may sound intuitive, but it is surprising how many times we’ve encountered companies performing their Cashbook reconciliation without considering results from the previous period. The key lesson here: it is important to live with your results (and calculations). The true power of a Cashbook Reconciliation summary is in rolling it forward; in other words, start by tying together Beginning Balance from the current period to Ending Balance of the previous period. Also, make sure to carry-forward any reconciliation discrepancies identified in the previous period to attempt resolution or continue ageing (see item #3 for more detail).

      3. Track and Age Discrepancies        

      The only sound method for identifying reconciliation discrepancies within the Cashbook process is to perform a transactional book-to-bank matching of bank statement items. This means bumping up collections recorded in the Servicing system, for example, and matching them with deposits on the bank statement. The benefits of this process are two-fold: (a) matching book-to-bank transactions certifies the bank statements (i.e. the backbone of the entire Custodial recon process); and (b) the process will reveal any true discrepancies /reconciling items in the Custodial account. Please remember to roll-forward any book items not matched against bank statement items (i.e. deposits in transit) for the following cycle.

      Adding some additional sophistication to the process, book-to-bank reconciliation could be performed on a daily basis. Bank statement data is available daily via BAI files and there are several reports in Black Knight and other Servicing platforms that provide daily activity, such as the T690 showing daily collections (i.e. daily version of the ZZ80). Performing this reconciliation on a daily basis catches issues quickly and allows those involved to correct the issue well before this becomes an outage in TOEC.

      As far as best practice – track and age any reconciliation discrepancies at the Cashbook level (even if you might be tracking certain outages “twice” if these are also identified in TOEC). Why? The majority of outages in Cashbook will fall under one of two main categories: (1) errors in movements of cash; or (2) true bank errors, such as incorrect settlement amounts. For these types of issues, communicating the discrepancy with corporate treasury, for example, will be more effective at the bank-account level. This, in turn, should reduce the turnaround time for resolution and possibly correct the item before initiating TOEC (particularly if performing this reconciliation daily).   

      4. Validate ALL Balances

      The clear figure to validate here is the Depository Bank Balance. The Depository Bank Balance should be composed of the ending bank balance on the bank statement PLUS any deposits (or withdrawals) in transit that are yet to settle in the account. If your process is already taking account point #3 above, this value should be simple to certify.

      Another important balance to validate is the depository balance according to the Servicing system. It may sound slightly counter-intuitive, but there could be a discrepancy between the calculated Depository Bank Balance and that which is presented in the Servicing system – think adjustments not entered correctly or manual transaction activity not recorded accurately (or at all) in the Servicing platform. We’ve found that it is best practice to perform a simple daily check to make sure both these values are in synch. 

      5. Track and Measure the Process.      

      All the considerations leading up to this one center on ensuring a sound Cashbook reconciliation, which is fantastic; however, visibility and metrics gathering over the process as it is happening in real-time distinguishes a proactive team vs. a reactive team. What’s the difference? A reactive team sees smoke and eventually reaches the fire with whatever tools happen to be on-hand to try to extinguish the flames. A proactive team sees the spark that started the fire. This level of visibility is afforded by adopting well-defined work assignments and developing a dashboard to track the resulting metrics. 

      We recommend doing what most companies already do: create a spreadsheet to assign analyst resources to specific Cashbook reconciliation, but we push it one step further by suggesting the inclusion of triggers to track the progress as it is happening. Create a spreadsheet or tool that listens for status changes in Cashbook reports (i.e. Pending, Submitted, Approved) as well as a means to collect metrics (i.e. number of items matched vs. outstanding) in an effort to get a meaningful pulse of the process as a whole. The development of the dashboard is certainly an evolutionary process; the trick is to subscribe to this mentality or management overview philosophy if the terminology is more fitting. Either way, evaluating the health of a process needs to occur as the process is happening and not after the process is completed – test this statement by applying it to a living body. Find creative metrics (and corresponding triggers) to track the process as it is unfolding to prevent a spark from becoming a forest fire.

      Below is an example of real-time processing metrics as offered within Integra Recon. To get the full picture, it is not only important to see the status of current work completed (left chart), but understanding when the bulk of the work was performed (right trend analysis).

      screenshot custodial reconciliation dashboard

      What considerations can you share about how you manage your Cashbook business process?

      [HousingWire] Embracing the future of mortgage servicing

      The following article appeared in the February 2021 issue of HousingWire.

      This year has brought plenty of disruption to mortgage servicing, from regulatory and economic uncertainties, to a long-term shift toward remote work environments. Meanwhile, the past decade has seen an explosion of digital solutions in mortgage origination, and servicing will inevitably follow suit. 

      In this context, it’s natural to consider digital transformation; as all our processes are upended, this is perhaps an ideal time to rethink the business, and the technologies that support that business.  

      But this is a decision to make with care. About 70% of digital transformations fail. The cause of these failures can often be traced back to not keeping the business goals at the forefront of the transformation process, or overlooking how technology impacts and interacts with the entire operational ecosystem. 

      It’s important to remember that digital transformation isn’t just about implementing new technology. It’s about strategically using technology to help you achieve your business goals. If your organization is looking for digital transformation, these tips will keep you on track for success. Continue reading on Housingwire>>

      Data challenges in mortgage servicing: Bank statements

      Bank statement information presents a data challenge for many Investor Accounting and Reporting teams. Created especially for automation, BAI files offer a great solution. Switching to BAI files provides a means to streamline multiple business processes, an important preparatory step in digital transformation.

      How to prepare a clearing account for audit in only 15 minutes

      Find out how a leading non-bank mortgage servicer streamlined the clearing reconciliation process with Integra RECON.

      Using Integra RECON to automate clearing account reconciliation, a top-20 non-bank mortgage servicer has substantially improved efficiency, consolidated operations, and introduced critical operational controls.

      Results

      • 50%: Reduction in FTE’s required to complete the process
      • 89%: Payment clearing transaction automatically matched by the system
      • 3: Number of checks manually cleared per day, instead of 400+
      • 30: Hours required to train a new FTE on the new process
      • 15: Minutes it takes to prepare an account for audit, instead of weeks

      Challenge

      The company’s clearing reconciliation process required 5 FTE’s to manage 7 accounts using spreadsheet solutions that offered limited quality control. And because there was no formalized process for clearing reconciliation, training new staff took several weeks. Each clearing account also came with its own specific challenges. For example, payment clearing required coordination of data from multiple sources and presented an unmanageable daily transaction volume. Disbursement clearing involved multiple touchpoints and heavy manual intervention, resulting in higher risk of errors.

      Solution

      Implementation of Integra RECON resulted in multiple key benefits:

      • The application-based process brings standardization and visibility. Introducing a single application was key to centralizing the function in one department and standardizing the process. Furthermore, the system’s workflow capabilities ensure easy oversight of the entire process.
      • Automation streamlines several aspects of the process. Thanks to automated data gathering and matching, analysts no longer spend time on tedious, error-prone tasks like collecting bank statements or manually entering data.
      • Built-in controls ensure processing integrity. The clearing reconciliation process no longer poses an audit concern, since built-in controls prevent unauthorized changes to data; timestamp analysts’ work; and keep analysts from submitting unbalanced reconciliations.
      • Audit preparation requires considerably less time. With Integra RECON, preparing for an audit now requires about fifteen minutes. Analysts simply print or export the appropriate reports directly from the application.

      Increase efficiency in custodial reconciliation by 69%

      See how top regional bank M&T reduced risk and saved 283 hours per month in custodial reconciliation processing with Integra RECON.

      To overcome the challenges and risks of a spreadsheet-based custodial reconciliation process, a leading regional bank partnered with Integra to implement Integra RECON. The bank is now well positioned to scale its custodial recon operations through loan acquisition.

      Results

      • 283.7: Hours saved using Integra RECON
      • 69.4%: Improvement from implementation of Integra RECON
      • 66.8%: Increase in efficiency for researching, finalizing and quality control
      • 76.2%: Decrease in time required for data gathering, import and integrity checks

      Challenge

      The bank’s former custodial reconciliation process relied on cumbersome spreadsheets. It was mostly
      manual, presenting multiple concerns for internal and external auditors:
      Integra BILLING delivers multiple key features for you and your subservicing team:

      • Errors and inefficiencies
      • Lack of visibility and controls
      • Limited repeatability and scalability

      Solution

      With these challenges in mind, the bank implemented Integra RECON for their loan portfolio. This represented a complete transformation of a key business function that increased productivity and reduced risk in three key areas:

      • Process automation and agility
      • Visibility and reporting
      • Audit and controls